arXiv Artificial Intelligence

Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholders

Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholders

Quick summary

arXiv:2609.29876v1 Announce Type: new Abstract: Neurosymbolic research typically assumes a pre-existing symbolic specification, leaving the upstream challenge of acquiring and formalizing requirements and constraints largely unaddressed. We present an architecture that fills this gap by using an OWL configuration ontology to mediate between neural constraint sources and downstream consumers. In this framework, LLM assistants elicit soft stakeholder preferences, while hardware specifications define hard physical and engineering limits. The ontology unifies these heterogeneous inputs, leverages

Key takeaways

  • arXiv:2609.29876v1 Announce Type: new Abstract: Neurosymbolic research typically assumes a pre-existing symbolic specification, leaving the upstream challenge of acquiring and formalizing requirements and constraints largely unaddressed.
  • We present an architecture that fills this gap by using an OWL configuration ontology to mediate between neural constraint sources and downstream consumers.
  • In this framework, LLM assistants elicit soft stakeholder preferences, while hardware specifications define hard physical and engineering limits.

Why it matters

This is more than a company headline: it shows who controls infrastructure, users and data in the AI value chain. The practical effect will appear in product integration, pricing and delivered capacity.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗